def expand_dims(input, axis=None, name=None, dim=None):
if dim is not None:
if axis is not None:
raise ValueError("cannot specify both 'axis' and 'dim'.")
axis = dim
return ops.ExpandDims(input, axis=axis, name=name)
def expand_dims(input, axis=None, name=None, dim=None):
"""
Inserts a dimension of 1 into a tensor's shape.
Given a tensor `input`, this operation inserts a dimension of 1 at the
dimension index `axis` of `input`'s shape. The dimension index `axis` starts
at zero; if you specify a negative number for `axis` it is counted backward
from the end.
This operation is useful if you want to add a batch dimension to a single
element. For example, if you have a single image of shape `[height, width,
channels]`, you can make it a batch of 1 image with `expand_dims(image, 0)`,
which will make the shape `[1, height, width, channels]`.
Other examples:
```python
# 't' is a tensor of shape [2]
shape(expand_dims(t, 0)) ==> [1, 2]
shape(expand_dims(t, 1)) ==> [2, 1]
shape(expand_dims(t, -1)) ==> [2, 1]
# 't2' is a tensor of shape [2, 3, 5]
shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5]
shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5]
shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]
```
This operation requires that:
`-1-input.dims() <= dim <= input.dims()`
This operation is related to `squeeze()`, which removes dimensions of
size 1.
Args:
input: A `Tensor`.
axis: 0-D (scalar). Specifies the dimension index at which to
expand the shape of `input`.
name: The name of the output `Tensor`.
dim: 0-D (scalar). Equivalent to `axis`, to be deprecated.
Returns:
A `Tensor` with the same data as `input`, but its shape has an additional
dimension of size 1 added.
"""
if dim is not None:
if axis is not None:
raise ValueError("cannot specify both 'axis' and 'dim'.")
axis = dim
return ops.ExpandDims(input, axis=axis, name=name)
def LayerSetup(self, bottom):
return _ops.ExpandDims(bottom, **self.arguments)
def Setup(self, bottom):
super(ExpandDimsLayer, self).Setup(bottom)
input = bottom[0] if isinstance(bottom, list) else bottom
return ops.ExpandDims(input, **self._param)